The Application of Knowledge-Based Clinical Decision Support Systems to Enhance Adherence to Evidence-Based Medicine in Chronic Disease

Among the technology-based solutions, clinical decision support systems (CDSSs) have the ability to keep up with clinicians with the latest evidence in a smart way. Hence, the main objective of our study was to investigate the applicability and characteristics of CDSSs regarding chronic disease. The Web of Science, Scopus, OVID, and PubMed databases were searched using keywords from January 2000 to February 2023. The review was completed according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses checklist. Then, an analysis was done to determine the characteristics and applicability of CDSSs. The quality of the appraisal was assessed using the Mixed Methods Appraisal Tool checklist (MMAT). A systematic database search yielded 206 citations. Eventually, 38 articles from sixteen countries met the inclusion criteria and were accepted for final analysis. The main approaches of all studies can be classified into adherence to evidence-based medicine (84.2%), early and accurate diagnosis (81.6%), identifying high-risk patients (50%), preventing medical errors (47.4%), providing up-to-date information to healthcare providers (36.8%), providing patient care remotely (21.1%), and standardizing care (71.1%). The most common features among the knowledge-based CDSSs included providing guidance and advice for physicians (92.11%), generating patient-specific recommendations (84.21%), integrating into electronic medical records (60.53%), and using alerts or reminders (60.53%). Among thirteen different methods to translate the knowledge of evidence into machine-interpretable knowledge, 34.21% of studies utilized the rule-based logic technique while 26.32% of studies used rule-based decision tree modeling. For CDSS development and translating knowledge, diverse methods and techniques were applied. Therefore, the development of a standard framework for the development of knowledge-based decision support systems should be considered by informaticians.


Introduction
In the last decades, evidence-based medicine (EBM) has been regarded as a standard of patient care. New drug treatments, the results of controlled trials, and all new clinical fndings are the main sources of evidence [1,2]. Clinical decision-making should be made based on such evidence to achieve the best outcomes. EBM is widely applied in chronic disease management. Although it has many benefts, EBM implementation in clinical settings is not easy and rarely applied in clinical practices [3]. Te most common barriers to applying EBM in routine care are how to access the latest evidence and the high volume of guidelines [4].
To achieve up-to-date evidence, clinicians must read a large volume of journals, articles, guidelines, and research outcomes daily. Because reviewing and memorizing them per day is a complex task for clinicians [5][6][7], clinical decision support systems (CDSSs) were developed to support clinicians to keep their knowledge up-to-date with the latest evidence in a smart way [8]. Te CDSSs referred to any computerized systems that are used to support healthcare providers in the patient-related decision-making process to bridge a gap between clinical practice and evidence-based medicine [2]. Broadly, CDSSs are classifed into two main categories to distinguish between their implementation and usage: knowledge-based and non-knowledge-based. Knowledge-based CDSSs use logical rules to produce outcomes in the form of recommendations to guide clinicians. Tere is always a source of knowledge in a knowledge-based CDSS, and rules are drawn from literature, patient-centered protocols, guidelines, or expert knowledge. Non-knowledgebased CDSS relies on machine learning or statistical pattern recognition techniques to simulate expert knowledge [2,9].
Chronic diseases are recognized as the main cause of morbidity, mortality, and cost worldwide. Tus, controlling and preventing chronic diseases have become the main focus of health systems [10]. To achieve the best possible outcomes, evidence-based chronic disease prevention (EBCDP) emerged as a new approach to developing evidence-based programs to ensure that healthcare providers have access to up-to-date scientifc evidence regarding chronic diseases [11]. Despite all eforts, there is a gap between knowledge generation and evidence implementation due to the inability of clinicians to search for and evaluate evidence [12]. Tere is a large volume of published studies describing the potential of knowledge-based CDSSs to increase evidence adherence in routine clinical practices [13][14][15]. In this regard, knowledge-based CDSSs have been developed to address the difculty of implementing evidence-based medicine at the point-of-care in chronic disease [16]. Due to the popularity and applicability of knowledge-based CDSS and the complexity of chronic disease management, investigating developed CDSS programs in chronic diseases was the main focus of this study. Investigating the characteristics, applications, outcomes, and most favorable techniques of these informatics-based technologies are the main objectives of our study.

Method
In this systematic review, the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) checklist was employed [17]. Published papers were retrieved from peer-reviewed journals from January 2000 to January 2023 based on search strategies in the following databases: PubMed, Ovid, Scopus, and Web of Science. Search strategies and extracted keywords were designed based on a formulated research question. Search strategies in each database are shown in Appendix A, Table A-1 in supplementary materials (available here). Te PEO (population, exposure, and outcome(s)) approach was utilized to formulate the research question. In this study, population refers to patients sufering from chronic disease, exposure refers to using the CDSS system, and outcomes refer to the efectiveness, capabilities, and features of applied tools.

Study Selection and Extraction
Criteria. Te inclusion criteria were as follows: (1) Studies that included chronic diseases, (2) studies that involved physicians in clinical decision support systems through patient management, (3) studies that focused on disease management or health promotion, (4) only studies that developed or used knowledge-based CDSS and no other types of CDSS, and (5) target groups and users were clinicians. Te exclusion criteria were as follows: (1) Studies that related to other domains except for health sciences, (2) studies that used only qualitative methods or simple usability tests, (3) studies that were published in a non-English paper, and (4) letter to the editor, reviews, and book chapters were not included in this study.

Data Extraction Process.
Two reviewers independently screened titles, abstracts, and full-text of articles in a stepwise process based on the PRISMA checklist to fnd relevant studies for our review. A third reviewer was consulted and made the last decision in case of disagreement. Irrelevant studies were excluded based on exclusion and inclusion criteria. Required data and information were extracted from the eligible articles based on predefned categories. Hence, an electronic data sheet was designed for entering data. Tese predefned categories include a year of publication, main objective, study design, country, disease type, features, source of knowledge, method of knowledge translation, applied platform, users, and the main results. Te gathered data were discussed by the authors.

Quality Appraisal.
Te Mixed Methods Appraisal Tool (MMAT) checklist was used for evaluating the appraisal of the eligible studies by two authors [18]. It contains fve diferent specifc questions for each study design to appraise articles. Each possible answer to each question comprises "Yes," "No," or "Cannot tell." Total scores of "Yes" answers are calculated and assumed as the quality score for each study. Papers without a minimum quality score of three were excluded from this review.

Results
A systematic database search yielded 206 citations. Te PRISMA fow diagram to describe the screening process is shown in Figure 1. It shows the process used to select the eligible studies that met the inclusion and exclusion criteria. In the frst phase of abstract screening, 51 articles were excluded based on inclusion and exclusion criteria. After the full-text screening, 57 journal articles and four conference papers were identifed as eligible studies. According to the MMAT checklist in the quality appraisal assessment phase, 24 studies were excluded due to low methodological quality. Tus, 38 studies were included in the synthesis and identifed as eligible studies. Te quality of 38 studies ranged from 60% to 100% which can be considered moderate to high. Of 38 articles, 11 articles scored between 60 and 70, 23 articles scored between 80 and 90, and 4 studies scored 100 based on the MMAT checklist.
Analysis of studies by year shows that the publication of studies has an almost upward trend. However, the rate of publication of articles in 2017 had the highest frequency with fve studies.
In terms of country, 38 studies were retrieved from sixteen countries. Accordingly, 17 studies were devoted to the American continent (44.74%), 11 studies were conducted in European countries (28.95%), eight studies were conducted in Asian countries (21.05%), and one study (2.63%) was conducted in Africa and one in Australia. Te distribution of studies by diferent countries is shown on the world map in Figure 2.
Tough the main purpose of all CDSSs was to support healthcare providers in the decision-making process with embedded knowledge in the system and lead to high-quality care for patients, the main approaches of all investigated CDSSs can be divided into eight main categories. Tey include enhancing early or accurate diagnosis or supporting clinicians in decision-making, improving adherence to standard guideline/expert advice, preventing medical errors and providing automatic advice based on the patient's electronic medical record, educating and training, providing up-to-date information to healthcare providers, identifying patients at high risk of severe disease, providing medical care remotely, standardize the process of care at the point-ofcare. Te frequency and percentage of these six approaches are described in Figure 3. Te details and characteristics of developed CDSSs in each study were described in Table 1.

Te Application of Knowledge-Based CDSSs in Various
Diseases. In medical sciences, all knowledge-based CDSS were developed to enhance patient care, diagnosis, followup, and routine care for a variety of diseases. Accordingly, 22 types of disease were investigated in this study. Asthma and cancer had the highest frequency among other diseases with a frequency of fve articles (13.16%). Various chronic diseases were in the next category (10.53%). COPD, diabetes, chronic headache, pediatric disorders, and pharmaceutical consultations with two studies (6.25%) are other diseases considered in the reviewed articles. Other diseases include multiple sclerosis, chronic allergy, chronic kidney disease (CKD), chronic heart disease, hypertension, chronic pain management, pancreatitis, congenital heart disease (CHD), Journal of Healthcare Engineering sleep disorder, COVID-9, eye disorders, osteoporosis, wound management, and thyroid nodules problems with one article (2.63%).  [36, 42-44, 46, 47, 57] to convert the clinical knowledge into CDSSs in the form of computerized systems.

Software Development Methods to Develop Knowledge-Based CDSSs.
After the embedded clinical knowledge was translated into the machine-interpretable format, nine software engineering methods were applied to implement these systems. However, three studies did not mention the    Journal of Healthcare Engineering 5  Journal of Healthcare Engineering 7  Journal of Healthcare Engineering 9   Journal of Healthcare Engineering applied techniques. In the following, we described the methods that were used in the reviewed articles: (1) User-centered design (UCD) approach with ten studies (26.32%): User-centered design or userdriven development is a framework in which users are involved at each stage of designing, developing, and developing a product, service, or process [58]. (2) Iterative approach with seven studies (18.42%): Iterative designing methodology is a circular developing process that models, evaluates, and tests all of the developing stages of software [59,60]. (3) SOA with six studies (15.79%): Service-Oriented Architecture (SOA) is a kind of software designing methodology in which some services are considered through a communication protocol over a network [59].
(5) BPM with two studies (5.26%): Business Process Modeling Notation (BPMN) is a software modeling method that models the various steps of developing software according to business process fow from end to end [60]. (7) Prototyping with one study (5.26%): Prototyping refers to a model in which a prototype would be built from the desired product. It has key features of the fnal product under design but does not intend to show the main logic of the ultimate program [59,60].
(8) RAD approach with one study (5.26%): Rapid application development (RAD) is an agile project management strategy that is popular in software development [59,60].
(9) Knowledge-to-Action framework with two studies (5.26%): It is a conceptual framework intended to help those concerned with knowledge translation deliver sustainable, evidence-based interventions [62].
(10) Workfow analysis with one study (3.13%): It is the process of breaking down the information fow of systems in the diferent workfow processes [63].
All of these applied techniques were developed on various platforms. Te majority of studies (n � 30) were developed in the form of web-based software (78.95%), three CDSSs were developed in the form of mobile-based applications (13.16%), one CDSSs was implemented in windowsbased software (2.63%), one of them was implemented as a simulated model (2.63%), and one CDSS was kind of web application which was accessible both in web-based form and in mobile-based application format (2.63%).

Features and Characteristics of Knowledge-Based CDSSs.
Te developed programs in the reviewed articles had different features and capabilities. Te various features of these programs are summarized in Figure 4. Some of these features were common in developed CDSSs, and others were more specifc to the purpose of the developed programs and the kind of disease. Te most common features among the knowledge-based CDSSs included providing guidance and advice for physicians (92.11%), generating patient-specifc recommendations (84.21%), integrating into electronic medical records (60.53%), and using alerts or reminders (60.53%). Self-management modules were only in programs integrated into telemedicine services (four studies).

Efectiveness of Developed Clinical Decision Support
Systems. We used the sign technique to investigate the effectiveness of developed CDSSs to improve decision-making in clinical processes by healthcare providers.
Of 32 studies, only four studies did not employ standard evaluation methods [31,40,45,46]. Accordingly, usability evaluation was conducted alongside the description of the CDSS development by applying diferent methods such as standard questionnaires (SUS, QUIS, etc.), and think-aloud methods [   To conduct the evaluation study, variant outcome measures or metrics were applied to the reviewed studies. All of these metrics can be classifed into three main categories: system performance, clinical outcomes, and clinical processes. Metrics related to system performance included accuracy (13 studies), usability level (11 studies), qualitative methods (10 studies), ease of use (eight studies), sensitivity (seven studies), specifcity (seven studies), odds ratio (six studies), ROC curve (three studies), NPV and PPV (two studies), error analysis (two studies), power estimation (two studies), alert fatigue (two studies), precision (one study), and resolution rate (one study).
Metrics related to clinical outcomes included clinical test enhancement such as Hb1AC level (13 studies), earlier diagnosis (four studies), and exacerbation rate of diseases such as emergency visits, oral steroid usage, and hospitalization (three studies).
Metrics related to clinical processes included overall process enhancement (12 studies), clinician feedback (13 studies), the rate of recorded clinical data before and after using the system (11 studies), system usage rate (eight studies), rate of completed tests and orders (seven studies), time of computation (six studies), system efcacy to enhance healthcare processes (six studies), new process acceptability (fve studies), simulation-based assessment (four studies), and the number of visits (one study).

Discussion
According to the literature review, knowledge-based CDSS can improve clinical decision-making based on the rules embedded in the knowledge-based system and deducted rules based on an inference engine generally, while nonknowledge-based systems rely only on machine learning techniques as one of the artifcial intelligence techniques to learn from stored data or historical experiences. In this review, we investigated the development of knowledgebased clinical decision support systems in chronic disease management regarding various characteristics and their applications. Our investigation showed that the main objective of all developed systems was to aid clinicians to make decisions with more confdence based on evidence. In this survey, knowledge-based CDSSs were considered computerbased and intelligent consultants.
However, various methods were employed to translate the clinical knowledge into machine-interpretable formats; logic rule-based algorithms; and rule-based decision tree techniques were the most widely used methods for developing knowledge-based clinical decision support systems in the reviewed articles. Tese two techniques are similar in terms of converting clinical protocols to decision rules. Tey have only difered in the representation of the extracted rules. In the decision tree approach, each branch represents a conditional statement (production rule) that can be easily understood by humans in addition to computer-based programs [27,53]. Tese rules are generated using literature-based evidence, clinical protocols, paper-based guidelines, or patient-based evidence [8].
Knowledge-based CDSSs are built on two main components: knowledge-based and inference engines. In the knowledge-based database of CDSS, all of the embedded knowledge is available in the form of IF-Ten rules [2,14]. Next, the inference engine runs the built-in logic of evidence based on the combination of predefned production rules and entered patient data. Te outputs of each CDSS contain alerts, diagnostic recommendations, probable risks, and treatment options [2].
Analysis of the most common features and capabilities in developed CDSSs showed that their functionality and applicability are diverse, including providing advice, alerts, patient-specifc recommendation, real-time guidance, calculating the risk of disease, and training capabilities. Since the focus of this study is only on knowledge-based CDSSs, all of the investigated programs were equipped with generating advice for physicians and providing guidance through clinical decision-making [64].
Out of 38 systems, 23 of them are integrated with electronic health records (EHR) or electronic medical records (EMR). Tis capability can enhance the functionality and applicability of such systems to support clinicians with real-time guidance [2]. Sutton et al. believed that integrating CDSS with the EHR could provide them with advanced functions [2]. Another capability of knowledge-based CDSS was related to providing healthcare providers with a probable patient-specifc diagnosis, treatment plan, or clinical advice based on the specifc conditions of each patient. Tis feature allows physicians to easily have a wide range of possible diagnoses based on the available evidence instead of memorizing them. Such smart systems and integration CDSSs with other types of intelligent systems such as computerized provider order entry (CPOE) aid clinicians in moving toward patient-centered care and enhance healthrelated decisions [65].
Investigating the efectiveness of applying CDSSs in routine clinical practices revealed that most of them have a positive impact on clinical decision-making in terms of various aspects. However, the evaluation of digital health interventions is complex [66]. A wide range of metrics and indicators are applied in published studies like in reviewed studies. Te represented metrics can be applied in further studies to measure the performance of developed CDSSs. Te positive impact of developed CDSSs on clinical outcomes and performance showed that clinicians can beneft from developed CDSSs in various aspects.
For CDSS development, various software development methods were applied, which were mentioned in the result section. Only two studies applied a specifc framework for system design and implementation [46,53]. Although the knowledge-to-action model is one of the most appropriate models for developing knowledge-based decision support systems [67], it has only been used in one study. Terefore, the development of a standard framework for developing knowledge-based decision support systems should be considered by informaticians. Summarizing the results, capabilities, and applied tools in the reviewed studies can lead to the generation of a general model for designing decision support systems in the chronic disease management domain. Tis model is shown in Figure 6.
Chronic diseases pose many challenges to health systems. More than 70% of the investigated systems achieved their goals successfully. Despite the fact that chronic diseases are broad, CDSSs have been developed in limited clinical domains. Evidence-based CDSSs for chronic diseases should be extended to diferent types of diseases to achieve the goal of the EBCDP approach. Managing patients with multimorbidity is a signifcant challenge for healthcare systems worldwide. Terefore, the development of decision support systems to manage multimorbid chronic diseases could be considered for further research in this feld.
Since this study is the frst attempt to review and analyze published articles regarding knowledge-based CDSSs in chronic diseases, it encounters some limitations. Te results of some studies are published in the form of reports, letters to the editor, or other types of studies. Tus, we have not considered them based on our exclusion criteria. Also, some researchers may apply CDSS in routine clinical practices, but they have not published their attempts in the form of any research article or conference paper. It accounts for a publication bias. Tus, further research on specifc domains in clinical practices may be done in the future.

Data Availability
Te study involves only a review of the literature without involving any data.

Ethical Approval
Te study protocol was reviewed and approved by the Ethical Committee at the Tehran University of Medical Sciences (IR.TUMS.VCR.REC.1397.115).

Conflicts of Interest
Te authors declare that they have no conficts of interest.